{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import keras\n",
    "from keras.preprocessing import image\n",
    "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Activation\n",
    "from keras.layers import Conv2D, AveragePooling2D\n",
    "from keras.models import Model, Sequential\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from keras import metrics\n",
    "\n",
    "from keras.models import model_from_json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/\n",
    "mat = scipy.io.loadmat('wiki_crop/wiki.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"dob\", \"photo_taken\", \"full_path\", \"gender\", \"name\", \"face_location\", \"face_score\", \"second_face_score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = mat['wiki'][0][0][0].shape[1]\n",
    "\n",
    "df = pd.DataFrame(index = range(0,instances), columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in mat:\n",
    "    if i == \"wiki\":\n",
    "        current_array = mat[i][0][0]\n",
    "        for j in range(len(current_array)):\n",
    "            #print(columns[j],\": \",current_array[j])\n",
    "            df[columns[j]] = pd.DataFrame(current_array[j][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>face_location</th>\n",
       "      <th>face_score</th>\n",
       "      <th>second_face_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>723671</td>\n",
       "      <td>2009</td>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Sami Jauhojärvi]</td>\n",
       "      <td>[[111.29109473290997, 111.29109473290997, 252....</td>\n",
       "      <td>4.300962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>703186</td>\n",
       "      <td>1964</td>\n",
       "      <td>[48/10000548_1925-04-04_1964.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Dettmar Cramer]</td>\n",
       "      <td>[[252.48330229530742, 126.68165114765371, 354....</td>\n",
       "      <td>2.645639</td>\n",
       "      <td>1.949248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711677</td>\n",
       "      <td>2008</td>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Marc Okrand]</td>\n",
       "      <td>[[113.52, 169.83999999999997, 366.08, 422.4]]</td>\n",
       "      <td>4.329329</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>705061</td>\n",
       "      <td>1961</td>\n",
       "      <td>[65/10001965_1930-05-23_1961.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Aleksandar Matanović]</td>\n",
       "      <td>[[1, 1, 634, 440]]</td>\n",
       "      <td>-inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720044</td>\n",
       "      <td>2012</td>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Diana Damrau]</td>\n",
       "      <td>[[171.61031405173117, 75.57451239763239, 266.7...</td>\n",
       "      <td>3.408442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dob  photo_taken                          full_path  gender  \\\n",
       "0  723671         2009  [17/10000217_1981-05-05_2009.jpg]     1.0   \n",
       "1  703186         1964  [48/10000548_1925-04-04_1964.jpg]     1.0   \n",
       "2  711677         2008    [12/100012_1948-07-03_2008.jpg]     1.0   \n",
       "3  705061         1961  [65/10001965_1930-05-23_1961.jpg]     1.0   \n",
       "4  720044         2012  [16/10002116_1971-05-31_2012.jpg]     0.0   \n",
       "\n",
       "                     name                                      face_location  \\\n",
       "0       [Sami Jauhojärvi]  [[111.29109473290997, 111.29109473290997, 252....   \n",
       "1        [Dettmar Cramer]  [[252.48330229530742, 126.68165114765371, 354....   \n",
       "2           [Marc Okrand]      [[113.52, 169.83999999999997, 366.08, 422.4]]   \n",
       "3  [Aleksandar Matanović]                                 [[1, 1, 634, 440]]   \n",
       "4          [Diana Damrau]  [[171.61031405173117, 75.57451239763239, 266.7...   \n",
       "\n",
       "   face_score  second_face_score  \n",
       "0    4.300962                NaN  \n",
       "1    2.645639           1.949248  \n",
       "2    4.329329                NaN  \n",
       "3        -inf                NaN  \n",
       "4    3.408442                NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datenum_to_datetime(datenum):\n",
    "    \"\"\"\n",
    "    Convert Matlab datenum into Python datetime.\n",
    "    :param datenum: Date in datenum format\n",
    "    :return:        Datetime object corresponding to datenum.\n",
    "    \"\"\"\n",
    "    days = datenum % 1\n",
    "    hours = days % 1 * 24\n",
    "    minutes = hours % 1 * 60\n",
    "    seconds = minutes % 1 * 60\n",
    "    exact_date = datetime.fromordinal(int(datenum)) \\\n",
    "           + timedelta(days=int(days)) \\\n",
    "           + timedelta(hours=int(hours)) \\\n",
    "           + timedelta(minutes=int(minutes)) \\\n",
    "           + timedelta(seconds=round(seconds)) \\\n",
    "           - timedelta(days=366)\n",
    "    \n",
    "    return exact_date.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date_of_birth'] = df['dob'].apply(datenum_to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>face_location</th>\n",
       "      <th>face_score</th>\n",
       "      <th>second_face_score</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>723671</td>\n",
       "      <td>2009</td>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Sami Jauhojärvi]</td>\n",
       "      <td>[[111.29109473290997, 111.29109473290997, 252....</td>\n",
       "      <td>4.300962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>703186</td>\n",
       "      <td>1964</td>\n",
       "      <td>[48/10000548_1925-04-04_1964.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Dettmar Cramer]</td>\n",
       "      <td>[[252.48330229530742, 126.68165114765371, 354....</td>\n",
       "      <td>2.645639</td>\n",
       "      <td>1.949248</td>\n",
       "      <td>1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711677</td>\n",
       "      <td>2008</td>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Marc Okrand]</td>\n",
       "      <td>[[113.52, 169.83999999999997, 366.08, 422.4]]</td>\n",
       "      <td>4.329329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>705061</td>\n",
       "      <td>1961</td>\n",
       "      <td>[65/10001965_1930-05-23_1961.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Aleksandar Matanović]</td>\n",
       "      <td>[[1, 1, 634, 440]]</td>\n",
       "      <td>-inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720044</td>\n",
       "      <td>2012</td>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Diana Damrau]</td>\n",
       "      <td>[[171.61031405173117, 75.57451239763239, 266.7...</td>\n",
       "      <td>3.408442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dob  photo_taken                          full_path  gender  \\\n",
       "0  723671         2009  [17/10000217_1981-05-05_2009.jpg]     1.0   \n",
       "1  703186         1964  [48/10000548_1925-04-04_1964.jpg]     1.0   \n",
       "2  711677         2008    [12/100012_1948-07-03_2008.jpg]     1.0   \n",
       "3  705061         1961  [65/10001965_1930-05-23_1961.jpg]     1.0   \n",
       "4  720044         2012  [16/10002116_1971-05-31_2012.jpg]     0.0   \n",
       "\n",
       "                     name                                      face_location  \\\n",
       "0       [Sami Jauhojärvi]  [[111.29109473290997, 111.29109473290997, 252....   \n",
       "1        [Dettmar Cramer]  [[252.48330229530742, 126.68165114765371, 354....   \n",
       "2           [Marc Okrand]      [[113.52, 169.83999999999997, 366.08, 422.4]]   \n",
       "3  [Aleksandar Matanović]                                 [[1, 1, 634, 440]]   \n",
       "4          [Diana Damrau]  [[171.61031405173117, 75.57451239763239, 266.7...   \n",
       "\n",
       "   face_score  second_face_score  date_of_birth  \n",
       "0    4.300962                NaN           1981  \n",
       "1    2.645639           1.949248           1925  \n",
       "2    4.329329                NaN           1948  \n",
       "3        -inf                NaN           1930  \n",
       "4    3.408442                NaN           1971  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = df['photo_taken'] - df['date_of_birth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove pictures does not include face\n",
    "df = df[df['face_score'] != -np.inf]\n",
    "\n",
    "#some pictures include more than one face, remove them\n",
    "df = df[df['second_face_score'].isna()]\n",
    "\n",
    "#check threshold\n",
    "df = df[df['face_score'] >= 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = ['name','face_score','second_face_score','date_of_birth','face_location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some guys seem to be greater than 100. some of these are paintings. remove these old guys\n",
    "df = df[df['age'] <= 100]\n",
    "\n",
    "#some guys seem to be unborn in the data set\n",
    "df = df[df['age'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>723671</td>\n",
       "      <td>2009</td>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711677</td>\n",
       "      <td>2008</td>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720044</td>\n",
       "      <td>2012</td>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>716189</td>\n",
       "      <td>2012</td>\n",
       "      <td>[02/10002702_1960-11-09_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>707745</td>\n",
       "      <td>1971</td>\n",
       "      <td>[41/10003541_1937-09-27_1971.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dob  photo_taken                          full_path  gender  age\n",
       "0  723671         2009  [17/10000217_1981-05-05_2009.jpg]     1.0   28\n",
       "2  711677         2008    [12/100012_1948-07-03_2008.jpg]     1.0   60\n",
       "4  720044         2012  [16/10002116_1971-05-31_2012.jpg]     0.0   41\n",
       "5  716189         2012  [02/10002702_1960-11-09_2012.jpg]     0.0   52\n",
       "6  707745         1971  [41/10003541_1937-09-27_1971.jpg]     1.0   34"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram = df['age'].hist(bins=df['age'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['age'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of output classes:  101\n"
     ]
    }
   ],
   "source": [
    "classes = 101 #(0, 100])\n",
    "print(\"number of output classes: \",classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_size = (224, 224)\n",
    "\n",
    "def getImagePixels(image_path):\n",
    "    img = image.load_img(\"wiki_crop/%s\" % image_path[0], grayscale=False, target_size=target_size)\n",
    "    x = image.img_to_array(img).reshape(1, -1)[0]\n",
    "    #x = preprocess_input(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pixels'] = df['full_path'].apply(getImagePixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>723671</td>\n",
       "      <td>2009</td>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711677</td>\n",
       "      <td>2008</td>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>[92.0, 97.0, 91.0, 89.0, 94.0, 90.0, 91.0, 96....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720044</td>\n",
       "      <td>2012</td>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "      <td>[61.0, 30.0, 10.0, 61.0, 30.0, 10.0, 61.0, 30....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>716189</td>\n",
       "      <td>2012</td>\n",
       "      <td>[02/10002702_1960-11-09_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "      <td>[97.0, 122.0, 178.0, 97.0, 122.0, 178.0, 97.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>707745</td>\n",
       "      <td>1971</td>\n",
       "      <td>[41/10003541_1937-09-27_1971.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "      <td>[190.0, 189.0, 194.0, 204.0, 203.0, 208.0, 203...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dob  photo_taken                          full_path  gender  age  \\\n",
       "0  723671         2009  [17/10000217_1981-05-05_2009.jpg]     1.0   28   \n",
       "2  711677         2008    [12/100012_1948-07-03_2008.jpg]     1.0   60   \n",
       "4  720044         2012  [16/10002116_1971-05-31_2012.jpg]     0.0   41   \n",
       "5  716189         2012  [02/10002702_1960-11-09_2012.jpg]     0.0   52   \n",
       "6  707745         1971  [41/10003541_1937-09-27_1971.jpg]     1.0   34   \n",
       "\n",
       "                                              pixels  \n",
       "0  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "2  [92.0, 97.0, 91.0, 89.0, 94.0, 90.0, 91.0, 96....  \n",
       "4  [61.0, 30.0, 10.0, 61.0, 30.0, 10.0, 61.0, 30....  \n",
       "5  [97.0, 122.0, 178.0, 97.0, 122.0, 178.0, 97.0,...  \n",
       "6  [190.0, 189.0, 194.0, 204.0, 203.0, 208.0, 203...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['age'].values\n",
    "target_classes = keras.utils.to_categorical(target, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features = df['pixels'].values\n",
    "features = []\n",
    "\n",
    "for i in range(0, df.shape[0]):\n",
    "    features.append(df['pixels'].values[i])\n",
    "\n",
    "features = np.array(features)\n",
    "features = features.reshape(features.shape[0], 224, 224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22578, 224, 224, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "features /= 255 #normalize in [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(features, target_classes\n",
    "                                        , test_size=0.30)#, random_state=42), stratify=target_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VGG-Face model\n",
    "model = Sequential()\n",
    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Convolution2D(2622, (1, 1)))\n",
    "model.add(Flatten())\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-trained weights of vgg-face model. \n",
    "#you can find it here: https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
    "#related blog post: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
    "model.load_weights('vgg_face_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#freeze all layers of VGG-Face except last 7 one\n",
    "for layer in model.layers[:-7]:\n",
    "    layer.trainable = False\n",
    "\n",
    "base_model_output = Sequential()\n",
    "base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)\n",
    "base_model_output = Flatten()(base_model_output)\n",
    "base_model_output = Activation('softmax')(base_model_output)\n",
    "\n",
    "age_model = Model(inputs=model.input, outputs=base_model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check trainable layers\n",
    "if False:\n",
    "    for layer in model.layers:\n",
    "        print(layer, layer.trainable)\n",
    "    \n",
    "    print(\"------------------------\")\n",
    "    for layer in age_model.layers:\n",
    "        print(layer, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "age_model.compile(loss='categorical_crossentropy'\n",
    "                  , optimizer=keras.optimizers.Adam()\n",
    "                  #, optimizer = sgd\n",
    "                  , metrics=['accuracy']\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(\n",
    "    filepath='classification_age_model.hdf5'\n",
    "    , monitor = \"val_loss\"\n",
    "    , verbose=1\n",
    "    , save_best_only=True\n",
    "    , mode = 'auto'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "enableFit = False\n",
    "\n",
    "if enableFit:\n",
    "    epochs = 250\n",
    "    batch_size = 256\n",
    "\n",
    "    for i in range(epochs):\n",
    "        print(\"epoch \",i)\n",
    "        \n",
    "        ix_train = np.random.choice(train_x.shape[0], size=batch_size)\n",
    "        \n",
    "        score = age_model.fit(\n",
    "            train_x[ix_train], train_y[ix_train]\n",
    "            , epochs=1\n",
    "            , validation_data=(test_x, test_y)\n",
    "            , callbacks=[checkpointer]\n",
    "        )\n",
    "        \n",
    "        scores.append(score)\n",
    "    \n",
    "    #restore the best weights\n",
    "    from keras.models import load_model\n",
    "    age_model = load_model(\"classification_age_model.hdf5\")\n",
    "    \n",
    "    age_model.save_weights('age_model_weights.h5')\n",
    "        \n",
    "else:\n",
    "    #pre-trained weights for age prediction: https://drive.google.com/file/d/1YCox_4kJ-BYeXq27uUbasu--yz28zUMV/view?usp=sharing\n",
    "    age_model.load_weights(\"age_model_weights.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_loss_change = []; loss_change = []\n",
    "for i in range(0, len(scores)):\n",
    "    val_loss_change.append(scores[i].history['val_loss'])\n",
    "    loss_change.append(scores[i].history['loss'])\n",
    "\n",
    "plt.plot(val_loss_change, label='val_loss')\n",
    "plt.plot(loss_change, label='train_loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6774/6774 [==============================] - 17s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.871919590848929, 0.24298789490543357]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loss and accuracy on validation set\n",
    "age_model.evaluate(test_x, test_y, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = age_model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_indexes = np.array([i for i in range(0, 101)])\n",
    "apparent_predictions = np.sum(predictions * output_indexes, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  4.651904340124004\n",
      "instances:  6774\n"
     ]
    }
   ],
   "source": [
    "mae = 0\n",
    "\n",
    "for i in range(0 ,apparent_predictions.shape[0]):\n",
    "    prediction = int(apparent_predictions[i])\n",
    "    actual = np.argmax(test_y[i])\n",
    "    \n",
    "    abs_error = abs(prediction - actual)\n",
    "    actual_mean = actual_mean + actual\n",
    "    \n",
    "    mae = mae + abs_error\n",
    "    \n",
    "mae = mae / apparent_predictions.shape[0]\n",
    "\n",
    "print(\"mae: \",mae)\n",
    "print(\"instances: \",apparent_predictions.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing model on a custom image\n",
    "\n",
    "Feed an image to find the apparent age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadImage(filepath):\n",
    "    test_img = image.load_img(filepath, target_size=(224, 224))\n",
    "    test_img = image.img_to_array(test_img)\n",
    "    test_img = np.expand_dims(test_img, axis = 0)\n",
    "    test_img /= 255\n",
    "    return test_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "picture = \"pacino.jpg\"\n",
    "#picture = \"brando.jpg\"\n",
    "#picture = \"katy-3.jpg\"\n",
    "\n",
    "prediction = age_model.predict(loadImage(picture))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(101)\n",
    "plt.bar(y_pos, prediction[0], align='center', alpha=0.3)\n",
    "plt.ylabel('percentage')\n",
    "plt.title('age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most dominant age class (not apparent age):  26\n",
      "apparent age:  26\n"
     ]
    }
   ],
   "source": [
    "img = image.load_img(picture)#, target_size=(224, 224))\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "print(\"most dominant age class (not apparent age): \",np.argmax(prediction))\n",
    "\n",
    "apparent_age = np.round(np.sum(prediction * output_indexes, axis = 1))\n",
    "print(\"apparent age: \", int(apparent_age[0]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
